NEFSC Trawl Bodymass Distribution
Adam A. Kemberling
2021-11-19
Exploring Size-at-age Changes
Loading Bio Data
# Access Biological Data with {gmRi}
nmfs_bio <- gmRi::gmri_survdat_prep(survdat_source = "bio")Fitting Von-Bertalanffy Growth
Data Prep
#### Data Prep ####
# Setting up for group analyses
# Rank species by how many measurements there are
species_abunds <- nmfs_bio %>% count(comname) %>% arrange(desc(n)) # ordered by number measured
# species to run it on:
vonbert_species <- sort(c("atlantic cod", "haddock", "summer flounder", "winter flounder", "acadian redfish",
"silver hake", "red hake", "american plaice", "yellowtail flounder",
"atlantic herring", "white hake", "butterfish","witch flounder",
"atlantic mackerel", "windowpane"))
names(vonbert_species) <- vonbert_species
seasons <- c("Summer", "Fall")
# Does not work for:
# skates, dogfish, fourspot flounder, goosefish
#### 5 year increments
##### pull species and make group id for later
species_data <- map(vonbert_species, function(species_name){
yr5_breaks <- seq(1970, 2020, by = 5)
yr5_ends <- seq(1974, 2024, by = 5)
yr5_labels <- str_c(yr5_breaks, "-", yr5_ends)
yr5_labels <- yr5_labels[1:(length(yr5_labels)-1)]
nmfs_bio %>%
filter(comname == species_name,
season %in% seasons,
is.na(age) == FALSE) %>%
mutate(five_yr_group = cut(est_year,
breaks = yr5_breaks,
labels = yr5_labels,
include.lowest = TRUE,
ordered_result = TRUE),
decade = floor_decade(est_year),
group_id = str_c(est_year, comname, sep = "-")) %>%
as.data.frame()
})Fitting to 5-year periods
Von-Bertalanffy parameterization:
( vb <- vbFuns(param="Typical") )function (t, Linf, K = NULL, t0 = NULL)
{
if (length(Linf) == 3) {
K <- Linf[[2]]
t0 <- Linf[[3]]
Linf <- Linf[[1]]
}
Linf * (1 - exp(-K * (t - t0)))
}
<bytecode: 0x7f934c9b8f60>
<environment: 0x7f9349191808># Function to Pull Vonbert Coefficients
vonbert_coef <- function(length_age_dat, start_points, min_obs = 20){
# Von Bert Fitting Using: {FSA}
# Don't run on fewer than a minimum number of obervations
num_aged <- nrow(length_age_dat)
if(num_aged < min_obs){
na_df <- data.frame("Linf" = NA, "K" = NA, "t0" = NA, "n_aged" = num_aged)
return(na_df)
}
# Use nls to estimate VBGF parameters using starting points
vbert_fit <- nls(length_cm ~ vb(age, Linf, K, t0),
data = length_age_dat,
start = start_points)
# Access parameters with coef()
vbert_coef <- as.data.frame(t(coef(vbert_fit))) %>%
mutate(n_aged = num_aged)
return(vbert_coef)
}# Function for running that for a single Species
species_vonbert <- function(comname, split_col, min_obs){
# Get starting points from all data
test_starts <- vbStarts(length_cm ~ age, data = species_data[[comname]])
# Get coefficients for groups
species_data[[comname]] %>%
split(.[split_col]) %>%
map_dfr(vonbert_coef, test_starts, min_obs = min_obs, .id = split_col) %>%
mutate(comname = comname)
}# Running everything?
species_coef <- vonbert_species %>%
map_dfr(species_vonbert, split_col = "five_yr_group", min_obs = 25)Plotting Coefficients
# pick species and coefficients
plot_vonbert_coef <- function(comnames, x_col, coef_name){
x_col_sym <- sym(x_col)
coef_sym <- sym(coef_name)
coef_label <- switch (
coef_name,
Linf = "Asymptotic Max Length (cm)",
K = "Body Growth Coefficient (K)",
t0 = "t0",
n_aged = "Number Aged"
)
species_coef %>%
filter(comname %in% comnames) %>%
ggplot(aes(y = {{coef_sym}}, x = {{x_col_sym}})) +
geom_line(group = 1, linetype = 3) +
geom_point(size = 0.8) +
scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
facet_wrap(~comname, ncol = 1, scales = "free") +
labs(x = "Date Period", y = coef_label)
}L-infinity
plot_vonbert_coef(comnames = c("atlantic cod", "haddock", "red hake"),
x_col = "five_yr_group",
coef_name = "Linf")
K
plot_vonbert_coef(comnames = c("atlantic cod", "silver hake", "red hake"),
x_col = "five_yr_group",
coef_name = "K")
t0
plot_vonbert_coef(comnames = c("atlantic cod", "silver hake"),
x_col = "five_yr_group",
coef_name = "t0")
Number Aged
plot_vonbert_coef(comnames = c("atlantic cod", "silver hake", "red hake"),
x_col = "five_yr_group",
coef_name = "n_aged")
Tracking Cohorts
Bubble Plots
# Plotting age distribution
plot_age_bubbleplot <- function(species){
species_data[[species]] %>%
count(comname, est_year, age) %>%
ggplot(aes(y = est_year, x = age, size = n)) +
geom_point(shape = 21, color = "white", fill = gmri_cols("gmri blue")) +
scale_y_reverse() +
facet_wrap(~comname) +
labs(y = "Year", x = "Age", size = "Number Measured") +
guides(size = guide_legend(title.position = "top", title.hjust = 0.5))
}
# Process Each species
bubble_plots <- map(.x = vonbert_species, .f = plot_age_bubbleplot)Atlantic Cod
bubble_plots[["atlantic cod"]]
Haddock
bubble_plots[["haddock"]]
Acadian Redfish
bubble_plots[["acadian redfish"]]
Winter Flounder
bubble_plots[["winter flounder"]]
Silver Hake
bubble_plots[["silver hake"]]
Ridgeplots
# Plotting age distribution as ggridges
plot_age_ridgeplot <- function(species){
species_data[[species]] %>%
mutate(yr = factor(est_year),
yr = fct_rev(yr)) %>%
ggplot(aes(x = age, y = yr)) +
geom_density_ridges(fill = gmri_cols("gmri blue"), color = "white") +
facet_wrap(~comname) +
scale_x_continuous(limits = c(0,NA)) +
labs(x = "Age", y = "Year") +
guides(size = guide_legend(title.position = "top", title.hjust = 0.5))
}
# Process Each species
ridge_plots <- map(vonbert_species, plot_age_ridgeplot)Atlantic Cod
ridge_plots[["atlantic cod"]]
Haddock
ridge_plots[["haddock"]]
Acadian Redfish
ridge_plots[["acadian redfish"]]
Winter Flounder
ridge_plots[["winter flounder"]]
